






import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
			from sagemaker import Session, TrainingJobAnalytics
			from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'  ,     'False'       )       ) is not True  ,     reason='Skipping test because should only be run when releasing minor transformers version'  ,     )
@pytest.mark.usefixtures('sm_env'       )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1600, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]       )
class 			lowerCAmelCase_							(					unittest.TestCase       ):




							def     __snake_case       ( self  : Union[str, Any]						):
											if self.framework == "pytorch":
															subprocess.run(
															    f'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split()			,				encoding='''utf-8'''			,				check=lowerCAmelCase__			,				)
											assert hasattr(self			,				'''env'''						)




							def     __snake_case       ( self  : Optional[int]			,				SCREAMING_SNAKE_CASE_  : List[Any]						):
											lowerCAmelCase__         =    {
											    '''enabled''': True,
											    '''processes_per_host''': 8,
											}
											lowerCAmelCase__         =    {
											    '''enabled''': True,
											    '''parameters''': {
											        '''microbatches''': 4,
											        '''placement_strategy''': '''spread''',
											        '''pipeline''': '''interleaved''',
											        '''optimize''': '''speed''',
											        '''partitions''': 4,
											        '''ddp''': True,
											    },
											}

											lowerCAmelCase__         =    {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

											lowerCAmelCase__         =    '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
											# creates estimator
											return HuggingFace(
											    entry_point=self.script			,				source_dir=self.env.test_path			,				role=self.env.role			,				image_uri=self.env.image_uri			,				base_job_name=f'{self.env.base_job_name}-{instance_count}-smp-{name_extension}'			,				instance_count=lowerCAmelCase__			,				instance_type=self.instance_type			,				debugger_hook_config=lowerCAmelCase__			,				hyperparameters={
											        **self.env.hyperparameters,
											        '''model_name_or_path''': self.model_name_or_path,
											        '''max_steps''': 500,
											    }			,				metric_definitions=self.env.metric_definitions			,				distribution=lowerCAmelCase__			,				py_version='''py36'''			,				)




							def     __snake_case       ( self  : Dict			,				SCREAMING_SNAKE_CASE_  : int						):
											TrainingJobAnalytics(lowerCAmelCase__						).export_csv(f'{self.env.test_path}/{job_name}_metrics.csv'						)




							@parameterized.expand([(1,)]						)
							def     __snake_case       ( self  : Dict			,				SCREAMING_SNAKE_CASE_  : List[Any]						):
											lowerCAmelCase__         =    self.create_estimator(lowerCAmelCase__						)

											# run training
											estimator.fit()

											# result dataframe
											lowerCAmelCase__         =    TrainingJobAnalytics(estimator.latest_training_job.name						).dataframe()

											# extract kpis
											lowerCAmelCase__         =    list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']						)
											lowerCAmelCase__         =    list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']						)
											# get train time from SageMaker job, this includes starting, preprocessing, stopping
											lowerCAmelCase__         =    (
											    Session().describe_training_job(estimator.latest_training_job.name						).get('''TrainingTimeInSeconds'''			,				999_999						)
											)

											# assert kpis
											assert train_runtime <= self.results["train_runtime"]
											assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy						)
											assert all(t <= self.results['''eval_loss'''] for t in eval_loss						)

											# dump tests result into json file to share in PR
											with open(f'{estimator.latest_training_job.name}.json'			,				'''w'''						) as outfile:
															json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}			,				lowerCAmelCase__						)










'''simple docstring'''



from __future__ import annotations
def        UpperCAmelCase (    A	:					list[int]       ,		A	:					int					):
							if len(A					) < k or k < 0:
														raise ValueError('''Invalid Input'''					)
							SCREAMING_SNAKE_CASE       :   Dict         =      sum(array[:k]					)
							for i in range(len(A					) - k					):
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      current_sum - array[i] + array[i + k]
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      max(A       ,		A					)
							return max_sum


if __name__ == "__main__":
							from doctest import testmod
							from random import randint

							testmod()
							lowerCAmelCase_  :							Optional[int]             =    [randint(-1000, 1000) for i in range(100)]
							lowerCAmelCase_  :							Optional[int]             =    randint(0, 110)
							print(f'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')




